A licensed EU electronic-money institution operating under the Lithuanian regulator came to us at the moment MiCA Phase 2 was reshaping the entire stablecoin issuer market. Inside four months we placed the brand at #1 across the four target prompts in ChatGPT, captured 41% share of voice on the MiCA prompt cluster in Perplexity, and shipped a quarterly industry report that became the single most-cited primary source on the topic in Q1 2026.
Methodology
- 01
Regulatory monitoring layer
RegulatoryMiCA Phase 2 timing meant the prompt universe was changing every two weeks. We ran weekly EBA / SEC / ESMA reviews, then re-ranked the prompt cluster monthly so the firm was always optimised for the live regulatory question, not last quarter's.
- 02
Original research as primary source
ResearchWe produced a forty-page industry report on stablecoin issuer requirements under MiCA — primary data, named contributors, real numbers. By month three it had been cited by twenty-seven tier-1 publications and was being returned as a primary source by ChatGPT and Perplexity.
- 03
Named regulatory experts
E-E-A-TThree named partners got full schema.org Person markup with verifiable sameAs to LinkedIn, the Bank of Lithuania register and a published bar admission. AI systems weight Person identities heavily for regulated content; this single move cleared E-E-A-T objections.
- 04
Prompt-pattern coverage
GEOWe mapped every conversational variant of each target prompt — "how to register MiCA CASP in Lithuania", "is there a faster MiCA path than Lithuania", "what's the cost of EMI plus MiCA". Forty-two prompt variants, one core content cluster.
- 05
Tier-1 PR with AI-trusted outlets
AuthorityTargeted placements in publications AI systems actively crawl as ground truth — niche regulatory journals, named industry indices, Wikidata entity update. No general media noise.
- 06
Crisis response on outdated answers
CrisisTwice in four months ChatGPT returned outdated information about the firm's licensing scope. Same-day correction pack — page update + schema redeploy + a piece in our content distribution chain — fixed both inside seventy-two hours.
What worked for the LLM extractor
- Industry report as a citable primary source for AI
- Schema.org Person with verifiable regulatory bios
- Weekly regulatory + content sync
- Conversational prompt variants, not single keywords
- Named expert quotes inside every priority page
What the LLM ignored
- Generic crypto-tier link building
- Hero copy without regulatory specifics
- Static content during a regulatory phase change
- Anonymous bylines on YMYL content
- Treating Wikipedia as one of many backlinks
Why this case is anonymised
The client operates in a phase of MiCA implementation where naming a winner shifts the competitive map. We will share names, full numbers and the deeper architecture on a discovery call under MNDA.
What stayed the same as a public case
The methodology is identical to the playbook on every Answerly engagement — prompt-research first, structural compliance second, citation building third, weekly tracking. What changes at the Enterprise tier is depth: an original research report, a regulatory monitoring layer, named partners with verifiable bios, and same-day crisis response when AI returns wrong answers about the firm.
Want this for a regulated business?
Enterprise tier, six-month minimum, dedicated team. With the crypto / fintech multiplier ×2.0 the headline rate is $17,800 / month. For most regulated EMIs and CASPs we see, that is one quarter of the AI-driven pipeline they unlock in the same window. The numbers above are not unusual; they are what disciplined Enterprise execution looks like.
Competitors out-ranked on tracked prompts
- Anonymised regulated competitor 1
- Anonymised regulated competitor 2
- Anonymised regulated competitor 3
Want a case like this for your brand?
Discovery call is free, 30 minutes, named lead, no SDR layer. We will show you your live LLM visibility and tell you what tier fits.